tinyML Talks: From the lab to the edge: Post-Training Compression

"From the lab to the edge: Post-Training Compression"

Edouard Yvinec
PhD student
Datakalab
Sorbonne Université

Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, going from tensorflow or torch to efficient DNN deployments on the edge remains one of the industry's biggest remaining challenges. During this presentation, we will see how Datakalab solves this problem, without using intensive computations nor re-training on the cloud, in two steps. First, we remain agnostic of the training framework by providing support for the inference of any DNN on a wide range of hardware. Second, we designed custom, state of the art, compression techniques that trely on post training quantization, pruning and context adaptation. The resulting inference models achieve remarkable speeds on microchips while staying within an accuracy loss of less than 1%.

Видео tinyML Talks: From the lab to the edge: Post-Training Compression канала The tinyML Foundation